1) It is easy to compute and understand.

2) It is well defined an ideal average should be.

3) It can also be computed in case of frequency distribution with open ended classes.

4) It is not affected by extreme values and also interdependent of range or dispersion of the data.

5) It can be determined graphically.

6) It is proper average for qualitative data where items are not measured but are scored.

7)It is only suitable average when the data are qualitative & it is possible to rank various items according to qualitative characteristics.

8) It can be calculated easily by watching the data.

9) In some cases median gives better result than mean.

Demerits :

1) For computing median data needs to be arranged in ascending or descending order.

2) It is not based on all the observations of the data.

3) It can not be given further algebraic treatment.

4) It is affected by fluctuation of sampling.

5) It is not accurate when the data is not large.

6) In some cases median is determined approximately as the mid-point of two observations whereas for mean this does not happen.

Demerits :

1) When extreme values are not given then it is used to measure the location.(for skewed distribution).

2) When measurement scale is ordinal that time median can be used.

3)For skewed data, median is used.

4)In some cases median gives us the accurate value than mean. For example, if we are considering the salary of people, if one's salary is more than its mean then so in such cases median is used.

2) It is well defined an ideal average should be.

3) It can also be computed in case of frequency distribution with open ended classes.

4) It is not affected by extreme values and also interdependent of range or dispersion of the data.

5) It can be determined graphically.

6) It is proper average for qualitative data where items are not measured but are scored.

7)It is only suitable average when the data are qualitative & it is possible to rank various items according to qualitative characteristics.

8) It can be calculated easily by watching the data.

9) In some cases median gives better result than mean.

Demerits :

1) For computing median data needs to be arranged in ascending or descending order.

2) It is not based on all the observations of the data.

3) It can not be given further algebraic treatment.

4) It is affected by fluctuation of sampling.

5) It is not accurate when the data is not large.

6) In some cases median is determined approximately as the mid-point of two observations whereas for mean this does not happen.

Demerits :

1) When extreme values are not given then it is used to measure the location.(for skewed distribution).

2) When measurement scale is ordinal that time median can be used.

3)For skewed data, median is used.

4)In some cases median gives us the accurate value than mean. For example, if we are considering the salary of people, if one's salary is more than its mean then so in such cases median is used.

Measures of central tendency

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